

import sys






def        snake_case__		(    lowerCamelCase_		):
							A      :					Tuple      		=					len(lowerCamelCase_		)
							A      :					List[Any]      		=					[[0 for x in range(lowerCamelCase_		)] for x in range(lowerCamelCase_		)]
							A      :					List[str]      		=					[[0 for x in range(lowerCamelCase_		)] for x in range(lowerCamelCase_		)]

							for chain_length in range(2     ,				lowerCamelCase_		):
														for a in range(1     ,				n - chain_length + 1		):
																					A      :					int      		=					a + chain_length - 1

																					A      :					List[Any]      		=					sys.maxsize
																					for c in range(lowerCamelCase_     ,				lowerCamelCase_		):
																												A      :					Any      		=					(
																												    matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
																												)
																												if cost < matrix[a][b]:
																																			A      :					List[Any]      		=					cost
																																			A      :					str      		=					c
							return matrix, sol






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_     ,				lowerCamelCase_		):
							if i == j:
														print('''A''' + str(lowerCamelCase_		)     ,				end=''' '''		)
							else:
														print('''('''     ,				end=''' '''		)
														print_optiomal_solution(lowerCamelCase_     ,				lowerCamelCase_     ,				optimal_solution[i][j]		)
														print_optiomal_solution(lowerCamelCase_     ,				optimal_solution[i][j] + 1     ,				lowerCamelCase_		)
														print(''')'''     ,				end=''' '''		)






def        snake_case__		(    ):
							A      :					int      		=					[30, 35, 15, 5, 10, 20, 25]
							A      :					List[str]      		=					len(lowerCamelCase_		)
							# Size of matrix created from above array will be
							# 30*35 35*15 15*5 5*10 10*20 20*25
							A		,		A      :					str      		=					matrix_chain_order(lowerCamelCase_		)

							print('''No. of Operation required: ''' + str(matrix[1][n - 1]		)		)
							print_optiomal_solution(lowerCamelCase_     ,				1     ,				n - 1		)


if __name__ == "__main__":
		main()









import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
		import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin







@require_torch
class 					__lowercase  (						unittest.TestCase		,       _SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							def       snake_case			(					self						)		->       Optional[Any]:
														A      :					Optional[Any]      		=					load_tool('''text-to-speech'''						)
														self.tool.setup()




							def       snake_case			(					self						)		->       Union[str, Any]:
														# SpeechT5 isn't deterministic
														torch.manual_seed(0						)
														A      :					Optional[Any]      		=					self.tool('''hey'''						)
														A      :					List[str]      		=					result.to_raw()
														self.assertTrue(
														    torch.allclose(
														        resulting_tensor[:3]    ,						torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)    ,						)						)






							def       snake_case			(					self						)		->       Optional[Any]:
														# SpeechT5 isn't deterministic
														torch.manual_seed(0						)
														A      :					int      		=					self.tool('''hey'''						)
														A      :					Any      		=					result.to_raw()
														self.assertTrue(
														    torch.allclose(
														        resulting_tensor[:3]    ,						torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)    ,						)						)






